Model Based Screening Embedded Bayesian Variable Selection for Ultra-high Dimensional Settings
Model Based Screening Embedded Bayesian Variable Selection for Ultra-high Dimensional Settings
We develop a Bayesian variable selection method, called SVEN, based on a hierarchical Gaussian linear model with priors placed on the regression coefficients as well as on the model space. Sparsity is achieved by using degenerate spike priors on inactive variables, whereas Gaussian slab priors are placed on the coefficients …